# NOT RUN {
#############################################################################
# EXAMPLE 1: Mathematics data | Gender DIF
#############################################################################
data( data.math )
dat <- data.math$data
items <- grep( "M", colnames(dat))
# estimate item parameters and WLEs
mod <- sirt::rasch.mml2( dat[,items] )
wle <- sirt::wle.rasch( dat[,items], b=mod$item$b )$theta
# assess DIF by logistic regression
mod1 <- sirt::dif.logistic.regression( dat=dat[,items], score=wle, group=dat$female)
# calculate DIF variance
dif1 <- sirt::dif.variance( dif=mod1$uniformDIF, se.dif=mod1$se.uniformDIF )
dif1$unweighted.DIFSD
## > dif1$unweighted.DIFSD
## [1] 0.1963958
# calculate stratified DIF variance
# stratification based on domains
dif2 <- sirt::dif.strata.variance( dif=mod1$uniformDIF, se.dif=mod1$se.uniformDIF,
itemcluster=data.math$item$domain )
## $unweighted.DIFSD
## [1] 0.1455916
# }
# NOT RUN {
#****
# Likelihood ratio test and graphical model test in eRm package
miceadds::library_install("eRm")
# estimate Rasch model
res <- eRm::RM( dat[,items] )
summary(res)
# LR-test with respect to female
lrres <- eRm::LRtest(res, splitcr=dat$female)
summary(lrres)
# graphical model test
eRm::plotGOF(lrres)
#############################################################################
# EXAMPLE 2: Comparison with Mantel-Haenszel test
#############################################################################
library(TAM)
library(difR)
#*** (1) simulate data
set.seed(776)
N <- 1500 # number of persons per group
I <- 12 # number of items
mu2 <- .5 # impact (group difference)
sd2 <- 1.3 # standard deviation group 2
# define item difficulties
b <- seq( -1.5, 1.5, length=I)
# simulate DIF effects
bdif <- scale( stats::rnorm(I, sd=.6 ), scale=FALSE )[,1]
# item difficulties per group
b1 <- b + 1/2 * bdif
b2 <- b - 1/2 * bdif
# simulate item responses
dat1 <- sirt::sim.raschtype( theta=stats::rnorm(N, mean=0, sd=1 ), b=b1 )
dat2 <- sirt::sim.raschtype( theta=stats::rnorm(N, mean=mu2, sd=sd2 ), b=b2 )
dat <- rbind( dat1, dat2 )
group <- rep( c(1,2), each=N ) # define group indicator
#*** (2) scale data
mod <- TAM::tam.mml( dat, group=group )
summary(mod)
#*** (3) extract person parameter estimates
mod_eap <- mod$person$EAP
mod_wle <- tam.wle( mod )$theta
#*********************************
# (4) techniques for assessing differential item functioning
# Model 1: assess DIF by logistic regression and WLEs
dif1 <- sirt::dif.logistic.regression( dat=dat, score=mod_wle, group=group)
# Model 2: assess DIF by logistic regression and EAPs
dif2 <- sirt::dif.logistic.regression( dat=dat, score=mod_eap, group=group)
# Model 3: assess DIF by Mantel-Haenszel statistic
dif3 <- difR::difMH(Data=dat, group=group, focal.name="1", purify=FALSE )
print(dif3)
## Mantel-Haenszel Chi-square statistic:
##
## Stat. P-value
## I0001 14.5655 0.0001 ***
## I0002 300.3225 0.0000 ***
## I0003 2.7160 0.0993 .
## I0004 191.6925 0.0000 ***
## I0005 0.0011 0.9740
## [...]
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Detection threshold: 3.8415 (significance level: 0.05)
##
## Effect size (ETS Delta scale):
##
## Effect size code:
## 'A': negligible effect
## 'B': moderate effect
## 'C': large effect
##
## alphaMH deltaMH
## I0001 1.3908 -0.7752 A
## I0002 0.2339 3.4147 C
## I0003 1.1407 -0.3093 A
## I0004 2.8515 -2.4625 C
## I0005 1.0050 -0.0118 A
## [...]
##
## Effect size codes: 0 'A' 1.0 'B' 1.5 'C'
## (for absolute values of 'deltaMH')
# recompute DIF parameter from alphaMH
uniformDIF3 <- log(dif3$alphaMH)
# compare different DIF statistics
dfr <- data.frame( "bdif"=bdif, "LR_wle"=dif1$uniformDIF,
"LR_eap"=dif2$uniformDIF, "MH"=uniformDIF3 )
round( dfr, 3 )
## bdif LR_wle LR_eap MH
## 1 0.236 0.319 0.278 0.330
## 2 -1.149 -1.473 -1.523 -1.453
## 3 0.140 0.122 0.038 0.132
## 4 0.957 1.048 0.938 1.048
## [...]
colMeans( abs( dfr[,-1] - bdif ))
## LR_wle LR_eap MH
## 0.07759187 0.19085743 0.07501708
# }
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